Authentication-based tracking

ABSTRACT

Methods and systems integrate digital fingerprint authentication-based identification and location tracking into a single, continuous process in which an authentication-integrated tracking system is simultaneously aware of both the identity and location of each physical object at all times as they move along a conveyance system. Insertion or removal of an object is quickly detected and reported. Rapid reestablishment and continuation of authentication-integrated tracking is enabled in the event of any temporary interruption or failure of tracking in the system. An exemplary system comprises plural tracking units networked together, each tracking unit including a camera or scanner to observe a corresponding field of view, the tracking units arrange to realize a continuous field of view of a physical conveyance system.

PRIORITY CLAIM

This application is a non-provisional of, and claims priority pursuantto 35 U.S.C. § 119(e) (2012) to U.S. provisional application No.62/377,436 filed Aug. 19, 2016, hereby incorporated by reference asthough fully set forth.

COPYRIGHT NOTICE

COPYRIGHT © 2016-2017 Alitheon, Inc. A portion of the disclosure of thispatent document contains material which is subject to copyrightprotection. The copyright owner has no objection to the facsimilereproduction by anyone of the patent document or the patent disclosure,as it appears in the Patent and Trademark Office patent file or records,but otherwise reserves all copyright rights whatsoever. 37 CFR § 1.71(d)(2017).

TECHNICAL FIELD

Centralized databases storing digital fingerprints of physical objectsenabling continuous authentication-integrated tracking with enhancedsecurity, rapid searching, and high reliability. Methods and apparatusto identify, track, and authenticate any physical object utilizing asuitable database.

BACKGROUND

Many different approaches are known to uniquely identify and trackphysical objects, including labeling and tagging strategies using serialnumbers, barcodes, holographic labels, RFID tags, and hidden patternsusing security inks or special fibers. All currently known methods relyon applied identifiers that are extrinsic to the object and, as such,may fail to detect introduction of counterfeit or otherwise unknownobjects. In addition, many applied identifiers add substantial costs tothe production and handling of the objects sought to be identified orauthenticated. Applied identifiers, such as labels and tags, are also atthemselves at risk of being damaged, lost, stolen, duplicated, orotherwise counterfeited.

SUMMARY OF THE PRESENT DISCLOSURE

The following is a summary of the present disclosure in order to providea basic understanding of some features and context. This summary is notintended to identify key or critical elements of the disclosure or todelineate the scope of the disclosure. Its sole purpose is to presentsome concepts of the present disclosure in simplified form as a preludeto a more detailed description that is presented later.

There are many known approaches to establishing or reestablishing theauthenticity of an object, including secure supply chains, expertassessment, and counterfeit detection. There are also many known methodsto tracking the location of physical objects. What is lacking, however,and is provided by the present disclosure, is the ability to integratedigital fingerprint authentication-based identification and locationtracking into a single continuous process in which anauthentication-integrated tracking system is simultaneously aware of theidentity and location of one or more physical objects at all times and,further, in which rapid reestablishment and continuation ofauthentication-integrated tracking is possible in the event of anytemporary interruption or failure of tracking.

Additional aspects and advantages of this disclosure will be apparentfrom the following detailed description of preferred embodiments, whichproceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the present disclosure can be obtained, amore particular description follows by reference to specific embodimentsthereof which are illustrated in the appended drawings. Understandingthat these drawings depict only typical embodiments of the disclosureand are not therefore to be considered to be limiting of its scope, thedisclosure will be described and explained with additional specificityand detail through the use of the accompanying drawings in which:

FIG. 1 is an example of an authentication region and fingerprinttemplate definition for a U.S. passport.

FIG. 2 is a simplified flow diagram of a process for authentication of aphysical object based on digital fingerprinting.

FIG. 3 is a simplified flow diagram of a process for authentication of apreviously fingerprinted object.

FIG. 4A shows an image of the numeral “3” representing the first digitin a serial number of an “original” or known U.S. dollar bill.

FIG. 4B shows an image of the numeral “3” representing the first digitin a serial number of a U.S. dollar bill to be authenticated.

FIG. 5A is an illustration of results of feature extraction showingselected locations of interest in the image of FIG. 4A.

FIG. 5B is an illustration of results of feature extraction showingselected locations of interest in the image of FIG. 4B.

FIG. 6A shows the same dollar bill image as in FIG. 4A, juxtaposed withFIG. 6B for comparison.

FIG. 6B shows an image of the numeral “3” that has been damaged ordegraded.

FIG. 7A shows detail of two fingerprint feature locations on the numeral3.

FIG. 7B shows detail of the damaged bill with the correspondingfingerprint feature locations called out for comparison.

FIG. 8 is a simplified illustration of a rotational transformation inthe process of comparing digital fingerprints of two images.

FIG. 9 is a simplified flow diagram of an induction-authenticationprocess.

FIG. 10 is a simplified flow diagram of one example of a process forcreating a digital fingerprint of a physical object.

FIG. 11 is a simplified system diagram to realize positive control ofobjects consistent with the present disclosure.

FIG. 12 is a simplified flow diagram of selected aspects of operation ofa tracking unit to realize positive control of objects.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Reference will now be made in detail to embodiments of the inventiveconcept, examples of which are illustrated in the accompanying drawings.The accompanying drawings are not necessarily drawn to scale. In thefollowing detailed description, numerous specific details are set forthto enable a thorough understanding of the inventive concept. It shouldbe understood, however, that persons having ordinary skill in the artmay practice the inventive concept without these specific details. Inother instances, well-known methods, procedures, components, circuits,and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first machine could be termed asecond machine, and, similarly, a second machine could be termed a firstmachine, without departing from the scope of the inventive concept.

It will be understood that when an element or layer is referred to asbeing “on,” “coupled to,” or “connected to” another element or layer, itcan be directly on, directly coupled to or directly connected to theother element or layer, or intervening elements or layers may bepresent. In contrast, when an element is referred to as being “directlyon,” “directly coupled to,” or “directly connected to” another elementor layer, there are no intervening elements or layers present. Likenumbers refer to like elements throughout. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

The terminology used in the description of the inventive concept hereinis for the purposes of describing particular embodiments only and is notintended to be limiting of the inventive concept. As used in thedescription of the inventive concept and the appended claims, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willalso be understood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed objects. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The methods described in the present disclosure enable theidentification of an object without the need for attaching, applying, orassociating physical tags or other extrinsic identifying materials withthe object. A system does this by creating a unique digital signaturefor the object, which is referred to as a digital fingerprint. Digitalfingerprinting utilizes the structure of the object, including randomand/or deliberate features created, for example, during manufacturing oruse of the object, to generate a unique digital signature for thatobject—similar to the way in which a human fingerprint references thefriction ridges on a finger. Also, like a human fingerprint, the digitalfingerprint can be stored and retrieved to identify objects at a latertime.

Eliminating the need to add extrinsic identifiers or any physicalmodifications to an object offers a number of advantages tomanufacturers, distributors, buyers, sellers, users, and owners ofgoods. Forgoing the addition of extrinsic identifiers reduces the costof manufacturing and offers greater security than physical tagging.Moreover, physical identifiers can be damaged, lost, modified, stolen,duplicated, or counterfeited whereas digital fingerprints cannot. Unlikeprior art approaches that simply utilize a comparison of pixels, asystem in accordance with the present disclosure utilizes the extractionof features to identify and authenticate objects. Feature extractionenables users to take a large amount of information and reduce it to asmaller set of data points that can be processed more efficiently. Forexample, a large digital image that contains tens of thousands of pixelsmay be reduced to a few locations of interest that can be used toidentify an object. This reduced set of data is called a digitalfingerprint. The digital fingerprint contains a set of fingerprintfeatures or locations of interest which are typically stored as featurevectors. Feature vectors make image processing more efficient and reducestorage requirements as the entire image need not be stored in thedatabase, only the feature vectors need to be stored. Examples offeature extraction algorithms include but are not limited to—edgedetection, corner detection, blob detection, wavelet features, Gabor,gradient and steerable output filter histograms, scale-invariant featuretransformation, active contours, shape contexts, and parameterizedshapes.

While the most common applications of the system may be in theauthentication of physical objects such as manufactured goods anddocuments, the system is designed to be applicable to any object thatcan be identified, characterized, quality tested, or authenticated witha digital fingerprint. These include but are not limited to mail pieces,parcels, art, coins, currency, precious metals, gems, jewelry, apparel,mechanical parts, consumer goods, integrated circuits, firearms,pharmaceuticals, and food and beverages. Here the term “system” is usedin a broad sense, including the methods of the present disclosure aswell as apparatus arranged to implement such methods.

The modern-day demand for increasingly higher levels of security alongtransportation and distribution networks calls for equally moderntracking systems that have continuous knowledge of the identity andlocation of each tracked object. In order to secure transportation of anobject to the maximum extent, a tracking system must be resilient to theremoval or insertion of an object, or the substitution of one object foranother (i.e. changing the identity of an object in the system). Inaddition, if tracking information is lost (e.g. during a systemshutdown) as may occur from time to time in even the most robustsystems, a secure system should be able to reestablish tracking as soonas possible, preferably automatically. Current approaches do notsimultaneously know the location and identity of each tracked object andmany lack robustness against stoppages, movement of objects on thetransport system, adulteration or substitution of one object foranother, and other common failures.

Track and trace. Many current technologies have the ability to determinethat an object is, or is likely to be, at a particular location along aconveyor system. The simplest (and still among the most common) areelectric eyes that detect a laser beam being broken. Other methods areeven more general. The baggage transport system at Seattle-TacomaInternational Airport (“Sea-Tac”), for example, counts the steps on thestepper motors that drive each of the conveyors along the transport pathand, through a series of calculations, determines where a bag placed onthe conveyor system at one place has been conveyed to. The systemcouples the stepper-motor tracking with electric eyes to determine whento switch a bag from one conveyor to another, for example with divertergates, in order to convey the bag to its designated destination.

Some forms of tracking incorporate primitive forms of itemidentification. All such identification is derivative in nature—that is,it represents proxy identification at best, as will be described.Referring again to the Sea-Tac system, bags have identifying tagsapplied at check-in and, occasionally, along their paths to the airplanehave those tags read by a barcode reader and identification is based onthe assumption that the bag tag, or other identification proxy, remainsattached to the same object it was originally affixed to.

None of these methods, however, maintain continuous awareness of boththe identity and location of the object. The taught system, as presentedbelow, does both. Existing systems do not maintain continuous awarenessof both the identity and location of a transported object because they,in general, have only sporadic information on where the object is infact located. This sporadic information may include check-i when theobject passes an electric eye, or when the object is scanned by abarcode reader. The rest of the time the object's location isapproximated or estimated through calculated assumptions (oftenerroneous) that the object is where the belt-tracking system projectsthat it is.

Further, the currently available technology does not have the ability toidentify the object itself. Some approaches may appear to do so. Thebarcode readers may, for example, determine that an object bearing aspecific barcode passed its location at a specific time. However,identifying an applied barcode or other identification proxy is muchless secure than identifying the object to which it is attached. Proxyidentification, as well as any proxy-associated security, is highlyvulnerable to circumvention by, for example, taking a tag from one bagand placing it on another (say one with dangerous contents), or bycreating a different tag with the same barcode, or by simply removing,damaging, or otherwise altering the tag.

Moreover, existing systems do not continuously track conveyed objects.Even where there is a laser or a camera for reading, say, a barcode, thefield of view is limited to a small section of a conveyor. Currentsystems do not offer continuous, uninterrupted control because thefields of view during which objects are being identified (typicallyindirectly by proxy) do not overlap, leaving gaps betweenidentifications where there is no control.

Commonly, such current systems have security cameras in place.Sometimes, all parts of the system are visible to a security camera.However, these cameras are not part of a system of location and identitycontrol. The cameras either require a person to monitor them to look forjams or unauthorized interference or use of them is made onlyretroactively for information regarding past occurrences.

In other words, the components of current systems that are able todetect an object can only do so by proxy (e.g. the barcode readers) orlocally (e.g. the electric eyes), while the components that can(collectively) see the entire system (e.g. the security cameras) are notcapable of identifying objects or tracking them individually orcontinuously.

Identification. Identification is the process of determining whichobject is or is most likely the one under inspection. Implicit is thedetection that there is (or is supposed to be) an item present. Currentsystems generally cannot identify an object directly at all. Currentsystems may infer an object's identity by, for example, the fact thatthe object occupies a location on a conveyor belt at which location anobject was placed at an earlier time, by assuming the object is the sameobject as originally placed on that location and that it has not beenreplaced or interfered with. On the other hand, they may infer itsidentity by proxy as is the case with the barcode or RFID tags placed onluggage.

In prior art, from check-in to baggage claim pickup, a piece of luggageis never positively and directly identified at any time. Apparatus andtechnology disclosed herein address that problem.

This disclosure, in one aspect, describes a method and technology forensuring positive control of items on assembly lines, conveyors or othermeans of transport or distribution. We define “positive control” to meanthat the system must have continuous knowledge of the identity of eachtracked object and of the location of each tracked object. Further, inthe event of loss of positive control, such a system must be capable ofrapid reestablishment of positive control, preferably automatic. Currenttechnologies meet none of these requirements. The taught system meetsall of them.

In one aspect, the present disclosure integrates into one action theascertaining of the location of an object and the positiveidentification of that object. The present disclosure thus differs fromcurrent technologies that solve part, but not all, of the problemssolved by the present disclosure.

Below reference will be made to baggage conveyor systems. Suchreferences are merely examples of a system on which the taughtdisclosure may operate. References to that system is not designed to belimiting of the taught technology.

In some other aspects, the present disclosure teaches a series oftechnologies that merge object identification and object tracking toprovide positive control of objects at all times. It enables at leastthe following capabilities and:

(1) Tracking. An object is continuously tracked, unless system failureoccurs (see below).

(2) Identification. The disclosed system has the capability tocontinuously identify individual objects as they move along in aphysical conveyance system so as to confirm that the object beingtracked is the object that is supposed to be tracked. In other words,the system is robust against substitution of one object (even an objectthat has the same serialization and that looks just like the originalobject) for another. Together, continuous tracking and continuousidentification provide continuous positive control.

(3) Restoration of positive control after system problems. Because thesystem of the present disclosure “knows” both where an object issupposed to go and which objects are presented to the tracking devices,it can with minimal or no human intervention direct the object towardits intended destination even when the tracking system shuts downentirely. The taught system has three major components:

An illustrative system may include a set of tracking units (TUs),capable of recovery from loss of tracking as further explained below.Preferably, a set of methods associated with each tracking unit realizesidentifying the objects it sees.

A tracking and identification database and system generally consists ofone or more networked tracking units. These tracking units may havescanners configured to capture images of a specified area in a facility.In general, the units are designed to work together to cover a broaderspecified area of the facility where items to be tracked are typicallyfound. There may be areas in this broader area that are not covered byimagers (such as inside X-ray inspection machines) but where the itemsare under some other kind of positive control. To help in tracking theobjects, images of the objects are captured and may be used directly fortracking and/or for extracting digital fingerprints for identifying theobjects in view. The tracking units may compare those digitalfingerprints with a previously-created reference database to identifythe items being tracked and/or they may add the digital fingerprints tosuch a database for use by downstream systems. A reference databasesystem (illustrated at 1170 in FIG. 11 in one example) may be used tostore data, update and query the reference database. The referencedatabase system may access one or more reference databases to supportand coordinate some operations of the tracking units. Additionalfunctions of a reference database system are described below.

The system described does not depend on the particulars of any of thesesystem components. Though both tracking and identification willgenerally be done with cameras (for example), all means of acquiringdata about an item and using that data to determine its movements is inview in this description provided such methods are consistent with theneed to provide concurrent object identification.

Scanning

In this application, the term “scan” is used in the broadest sense,referring to any and all means for capturing an image or set of images,which may be in digital form or transformed into digital form. Imagesmay, for example, be two dimensional, three dimensional, or in the formof a video. Thus a “scan” may refer to an image (or digital data thatdefines an image) captured by a scanner, a camera, a specially adaptedsensor or sensor array (such as a CCD array), a microscope, a smartphonecamera, a video camera, an x-ray machine, a sonar, a radar, a lidar, anultrasound machine, a microphone (or other instruments for convertingsound waves into electrical energy variations), etc. Broadly, any devicethat can sense and capture either electromagnetic radiation ormechanical wave that has traveled through an object or reflected off anobject or any other means to capture surface or internal structure of anobject is a candidate to create a “scan” of an object. Various means toextract “fingerprints” or features from an object may be used; forexample, through sound, physical structure, chemical composition, ormany others. The remainder of this application will use terms like“image” but when doing so, the broader uses of this technology should beimplied. In other words, alternative means to extract “fingerprints” orfeatures from an object should be considered equivalents within thescope of this disclosure. Similarly, terms such as “scanner” and“scanning equipment” herein may be used in a broad sense to refer to anyequipment or capture devices capable of carrying out “scans” as definedabove, or to equipment that carries out “scans” as defined above as partof their function.

Authenticating

In this application, different forms of the words “authenticate” and“authentication” will be used broadly to describe both authenticationand attempts to authenticate which comprise creating a digitalfingerprint of the object. Therefore, “authentication” is not limited tospecifically describing successful matching of inducted objects orgenerally describing the outcome of attempted authentications. As oneexample, a counterfeit object may be described as “authenticated” evenif the “authentication” fails to return a matching result. In anotherexample, in cases where unknown objects are “authenticated” withoutresulting in a match and the authentication attempt is entered into adatabase for subsequent reference the action described as“authentication” or “attempted authentication” may also, post facto,also be properly described as an “induction”. An authentication of anobject may refer to the induction or authentication of an entire objector of a portion of an object.

Authentication Regions

Because digital fingerprinting works with many different types ofobjects, it may be useful to define what regions of digital images ofobjects are to be used for the extraction of features for authenticationpurposes. The chosen regions may vary for different classes of objects.In some embodiments, a chosen region may be the image of the entireobject; in other embodiments chosen regions may be one or moresub-regions of the image of the object.

For instance, in the case of a photograph, a digital image of the entirephotograph may be chosen for feature extraction. Each photograph isdifferent and there may be unique feature information anywhere in aphotograph. In such a case, the authentication region may be the entirephotograph.

In some embodiments, multiple regions may be used for fingerprinting. Insome examples, there may be several regions where significant variationstake place among different similar objects that need to be distinguishedwhile, in the same objects, there may be regions of little significance.In other examples, a template may be used (see FIG. 1 ) to defineregions of interest, including elimination of regions of littleinterest.

In one embodiment, an object, such as a bank note, may be deemedauthenticated if a few small arbitrary regions scattered across thesurface are fingerprinted, possibly combined with one or morerecognitions of, for example, the contents of a region signifying thevalue of the bank note or one containing the bank note serial number. Insuch examples, the fingerprints of any region (along with sufficientadditional information to determine the bank note value and itspurported identity) may be considered sufficient to establish theauthenticity of the bill. In some embodiments, multiple fingerprintedregions may be referenced in cases where one or more region may beabsent from an object (through, for example, tearing) when, for example,a bank note is presented for authentication. In other embodiments,however, all regions of an object may need to be authenticated to ensurean object is both authentic and has not been altered.

In one embodiment, a passport may provide an example of featureextractions from multiple authentication regions. In the case of apassport, features chosen for authentication may be extracted fromregions containing specific identification information such as thepassport number, the recipient name, the recipient photo, etc., asillustrated in FIG. 1 . In some examples, a user may define a featuretemplate specifying the regions whose alteration from the original wouldinvalidate the passport, such as the photo, identifying personal data,or other regions considered important by the user. More details offeature templates are given in Ross, et at. U.S. Pat. No. 9,443,298.

FIG. 1 illustrates one example of an authentication region and afingerprint template definition for a U.S. passport. In this figure,brace 101 refers to a simplified flow diagram of a process as follows:At process block 102, an object is scanned to generate an “originalimage”, that is, a digital image file or a digital data file in anysuitable format that is herein simply referred to as an “image”. Theoriginal image is illustrated as the data page spread of a U.S. passportbook, at block 150.

Next, the system processes the image data to determine an authenticationregion. In this example, the authentication region is the biographicdata page of the U.S. Passport, located in the lower portion of image150, identified by dashed box 154. Next, the process generates anauthentication image for feature extraction, block 106. Theauthentication image is illustrated at reference 156. Next, at block108, the process defines one or more locations of interest for featurevector extraction. The locations of interest in this example are, asshown in image 158 by dashed boxes 160, the surname, the given name, thepassport number, and the passport photo.

Finally, at block 110, the process 100 comprises creating a fingerprinttemplate 120. In this example, template 120 identifies an object class(U.S. Passport), defines an authentication region (for example, by X-Ycoordinates), and lists one or more locations of interest within thatauthentication region. In this instance, the list comprises passportnumber, photo, first name, and last name.

In some embodiments, an ability to define and store optimalauthentication regions for classes of objects may offer benefits to auser. In some embodiments, it may be preferable to scan limited regionsof objects rather than to scan entire objects. For instance, in the caseof an article of designer clothing, scanning limited area, such as aclothing label, for feature vectors to generate a digital fingerprintmay be preferable to scanning an entire garment. Further, defining suchregions may enable detection of partial alteration of an object.

Once an authentication region is defined, specific applications may becreated for different markets or classes of objects that may assistusers in locating and scanning an optimal authentication region. In someembodiments, for example when utilizing a mobile device, a location boxand crosshairs may automatically appear in the viewfinder of asmartphone camera application, to help the user center the camera on anauthentication region, and automatically lock onto a region and completea scan when the device is focused on an appropriate area. It should benoted that, although some examples suggested above are two-dimensionalobjects (passport, bank note), the present disclosure is fullyapplicable to three-dimensional objects as well. As previously noted,scanning may be of any kind, including 2-D, 3-D, stereoscopic, HD, etc.and is not limited to the use of visible light or to the use of light atall (as previously noted, sonar and ultrasound are, for example,appropriate scanning technologies).

In some embodiments, objects may have permanent labels or otheridentifying information attached to them. In addition to the objectsthemselves, these attachments may also be referenced as features fordigital fingerprinting, particularly where the label or otheridentifying information becomes a permanent part of the object. In oneexample, a permanent label may be used as an authentication region forthe object to which it is affixed. In another example, a label may beused in conjunction with the object itself to create a fingerprint ofmultiple authentication regions referencing both a label and an objectto which the label is affixed.

In one example, wine may be put into a glass bottle and a label affixedto the bottle. Since it is possible that a label may be removed andre-applied elsewhere merely using the label itself as an authenticationregion may not be sufficient. In this case, the authentication regionmay be defined so as to include both a label and a substrate it isattached to—in this example some portion of a label and some portion ofa glass bottle. This “label and substrate” approach may be useful indefining authentication regions for many types of objects, such asvarious types of goods and associated packaging. In other instances,authentication may reveal changes in the relative positions of someauthentication regions such as in cases where a label has been movedfrom its original position, which may be an indication of tampering orcounterfeiting. If an object has “tamper-proof” packaging, this may alsobe included in the authentication region.

In some embodiments, multiple authentication regions may be chosen fromwhich to extract unique features. In a preferred embodiment, multipleauthentication regions may be selected to enable the separateauthentication of one or more components or portions of an object. Forexample, in one embodiment, features may be extracted from two differentparts of a firearm. Both features may match the original firearm butsince it is possible that both parts may have been removed from theoriginal firearm and affixed to a weapon of different quality, it mayalso be useful to determine whether the relative positions of the partshave changed. In other words, it may be helpful to determine that thedistance (or other characteristics) between Part A's authenticationregion and Part B's authentication region remains consistent with theoriginal feature extraction. If the positions of Parts A and B are foundto be consistent to the relative locations of the originalauthentication regions, the firearm may be authenticated. Specificationsof this type may be stored with or as part of a digital fingerprint ofan object.

Fingerprint Template Definition

In an embodiment, when a new type or class of object is being scannedinto a system for the first time, the system can create a fingerprinttemplate (see FIG. 1 ) that can be used to control subsequentauthentication operations for that class of objects. This template maybe created either automatically by the system or by a human-assistedprocess.

A fingerprint template is not required for the system to authenticate anobject, as the system can automatically extract features and create adigital fingerprint of an object without it. However, the presence of atemplate may optimize the authentication process and add additionalfunctionality to the system.

TABLE 1 Example Fingerprint Template. CLASS: [Description of the object]United States Passport AUTHENTICATION REGION: [Description of theauthentication regions for the object] Region 1: (x1, y1, z1), (x2, y2,z2) . . . Region n REGION MATCH LIST [List of the regions that arerequired to match to identify an object] Region List: 1 . . . nFEATURES: [Key features of the object] Feature 1: Passport NumberFeature 2: Photo Feature 3: First Name Feature 4: Last Name . . .Feature n METHODS: [Programs that can be run on features of an object]Feature 2: Photo Method 1: [checkphoto.exe] Check for uneven edgesindicating photo substitution . . . Method n Feature n Method nADDITIONAL DATA [Additional data associated with the object] Data 1:example data . . . Data n

The uses of the fingerprint template include but are not limited todetermining the regions of interest on an object, the methods ofextracting fingerprinting and other information from those regions ofinterest, and methods for comparing such features at different points intime. The name “fingerprint template” is not important; other data withsimilar functionality (but a different name) should be consideredequivalent.

In an embodiment, four different but related uses for this technologyare particularly in view in the present disclosure. These areillustrative but are not intended to be limiting of the scope of thedisclosure. These applications may be classified broadly as (1)authentication of a previously scanned original, (2) detection ofalteration of a previously scanned original, (3) detection of acounterfeit object without benefit of an original, and (4) assessing thedegree to which an object conforms with a predetermined specification,such as a manufacturing specification or other applicable specification.

The uses of the fingerprint template include but are not limited todetermining the regions of interest on an object, the methods ofextracting fingerprinting and other information from those regions ofinterest, and methods for comparing such features at different points intime. The name “fingerprint template” is not important; other data withsimilar functionality (but a different name) should be consideredequivalent.

In an embodiment, four different but related uses for this technologyare particularly in view in the present disclosure. These areillustrative but are not intended to be limiting of the scope of thedisclosure. These applications may be classified broadly as (1)authentication of a previously scanned original, (2) detection ofalteration of a previously scanned original, (3) detection of acounterfeit object without benefit of an original, and (4) assessing thedegree to which an object conforms with a predetermined specification,such as a manufacturing specification.

In example (1), an object is fingerprinted preferably during thecreation process (or at any time when its provenance may be sufficientlyascertained) or at a point where an expert has determined itsauthenticity. Subsequently, the object is later re-fingerprinted, andthe two sets of fingerprints are compared to establish authenticity ofthe object. The fingerprints may be generated by extracting a singlefingerprint from the entire object or by extracting multiple sets offeatures from multiple authentication regions. Fingerprinting may alsoinvolve reading or otherwise detecting a name, number, or otheridentifying characteristics of the object using optical characterrecognition or other means which may be used to expedite or facilitate acomparison with other fingerprints. For instance, in cases wheremanufacturing (or other object) databases use serial numbers or otherreadable identifiers, such identifiers may be utilized to directlyaccess the database record for the object and compare its digitalfingerprint to the original that was previously stored, rather thansearching an entire digital fingerprinting database for a match.

In case (2), a fingerprinted object is compared, region by region, witha digital fingerprint of an original object to detect low or nonexistentmatching of the fingerprint features from those regions. While case (1)is designed to determine whether the original object is now present,case (2) is designed to detect whether the original object has beenaltered and, if so, how it has been altered. In some embodiments,authentication regions having poor or no matching fingerprint featureswill be presumed to have been altered.

In case (3), an object may not have been fingerprinted while itsprovenance was sufficiently ascertainable. One example would be bills orpassports created prior to initiating the use of a digitalfingerprinting system. In such examples, digital fingerprints of certainregions of interest on an object may be compared with digitalfingerprints from known, or suspected, counterfeit objects or with boththose and fingerprints of properly authenticated objects. In oneexample, a photograph may be spuriously added to a passport and, as anartifact of the counterfeiting, the edge of the added photo may tend tobe sharper than an edge of an original, unaltered, photograph. In such acase, fingerprint characteristics of known authentic passports and thoseof passports that are known (or suspected to) have been altered bychanging a photograph may be compared with the passport being inspectedto estimate whether the passport exhibits indications of alteration.

Digital Fingerprint Generation

In an embodiment, once an object has been scanned and at least oneauthentication region has been identified, the digital image, which willbe used to create the unique digital fingerprint for the object, isgenerated. The digital image (or set of images) provides the sourceinformation for the feature extraction process.

In the present disclosure, a digital fingerprinting feature is definedas a feature or a location of interest in an object, which feature isinherent to the object itself. In some embodiments, features preferablyare a result of a manufacturing process, other external processes, or ofany random, pseudo-random, or deliberate process or force, such as use.To give one example, gemstones have a crystal pattern which provides anidentifying feature set. Every gemstone is unique and every gem stonehas a series of random flaws in its crystal structure. This pattern ofrandom flaws may be used for the extraction of feature vectors foridentification and authentication.

In the present disclosure, a “feature” is not necessarily concerned withreading or recognizing meaningful content, for example by using methodslike optical character recognition. A digital fingerprint of an objectmay capture both features of the object and features of any identifiersthat are affixed or attached to the object. Feature vectors extractedfrom authentication regions located on an affixed identifier are basedon the substances of which the identifier is physically comprised ratherthan the information (preferably alphanumeric) that is intended to becommunicated by the identifier. For instance, in the case of a winebottle, features may be captured from the bottle and from a labelaffixed to the bottle. If the label includes a standard UPC bar code,the paper of the label and the ink pattern of the bar code may be usedto extract a feature vector without reading the alphanumeric informationreflected by the bar code. An identifier, such as a UPC bar code printconsisting of lines and numbers, has no greater significance in thegeneration and use of a feature vector than a set of randomly printedlines and numbers.

Although reading identifier information is not necessary for digitalfingerprinting, in some embodiments, where a user desires to capture orstore identifier information (such as a name, serial number, or a barcode) in an association with an object, the system may allow the user tocapture such information and store it in the digital fingerprint.Identifier information may, for example, be read and stored by utilizingtechniques such as optical character recognition, and may be used tofacilitate digital fingerprint comparisons. In some cases, serialnumbers may be used as the primary index into a database that may alsocontain digital fingerprints. There may be practical reasons forreferencing serial numbers in relations to digital fingerprints. In oneexample, a user is seeking determine whether a bank note is a match witha particular original. In this case, the user may be able to expeditethe comparison by referencing the bank note serial number as an indexinto the digital fingerprinting database rather than iterating through alarge quantity of fingerprints. In these types of cases, the indexrecognition may speed up the comparison process but it is not essentialto it.

Once a suitable digital fingerprint of an object is generated thedigital fingerprint may be stored or registered in a database. Forexample, in some embodiments, the digital fingerprint may comprise oneor more fingerprint features which are stored as feature vectors. Thedatabase should preferably be secure. In some embodiments, a uniqueidentifier, such as a serial number, may also be assigned to an objectto serve, for example, as a convenient index. However, assigning aunique identifier is not essential as a digital fingerprint may itselfserve as a key for searching a database independent of any addition of aunique identifier. In other words, since a digital fingerprint of anobject identifies the object by the unique features and characteristicsof the object itself the digital fingerprint renders unnecessary the useof arbitrary identifiers such as serial numbers or other labels andtags, etc.

FIG. 2 represents an example of a simplified flow diagram of a process200 for authenticating or identifying an object using digitalfingerprinting using a U.S. passport for illustration for part of theprocess. The process begins with scanning the object, block 202. Animage 250 is acquired, in this illustration the front page of a U.S.passport is used. The next step is to determine a class of the object,block 204. This step may be omitted where the class is known. Forexample, at a border, a station may be in use that only checks U.S.passports. In another example, the system may be at a passport printingfacility. Thus, the class of objects may be known a priori.

Next, at block 206, a database query may be conducted to see if atemplate exists in the system for the object that was scanned at 202.For example, in some cases, the initial image may be processed toextract a serial number or other identifying information. In anembodiment, the database may then be interrogated; decision 206, to seeif a template exists for that serial number. If the answer is YES, path208, the system accesses the template 212 and uses it to select one ormore authentication regions 210. The template 212 lists the regions andtheir respective locations in the image (i.e. on the passport front pagein this example). Physical locations may, as an example, be specifiedrelative to a given location, and/or relative to each other. Locationmay be important because, for example, a replaced photograph may not bein exactly the same location as the removed original. In short, thetemplate guides the authentication software in analyzing the image data.In that analysis, for each authentication region (called a “Feature” in212), various features are extracted from the image data, block 222.

The extracted features are used to form a digital fingerprint of theobject, block 224. For example, each feature may be described by afeature vector. Location and other data and metadata may be included inthe fingerprint. In general, the process for extracting features anddescribing them in feature vectors may be specified in the template. Thetemplate may also specify which regions must be matched to declare thepassport a match. In the passport example, all specified regions mustmatch a record in the database for the passport to be determined to beauthentic and unaltered. In other cases, a few matches may besufficient. The digital fingerprint generated at block 224 is then usedto query a reference database 230 for a match.

Returning to the decision block 206, there may not be an existingtemplate in the system for the object under inspection—NO branch for“Non-Template Object Class.” The process here may vary with the type ofobject under inspection and the purpose for the inspection. In somecases, a scanned image of an object may be processed to find locationsof interest, block 232, for example, surface areas that arenon-homogenous and thus have considerable image data content. In otherwords, finding locations of interest may be automated or semi-automated.The locations may be used to extract features, block 234 and/or recordedin a template for later use. Preferably, locations should be recordedin, or otherwise associated with, the digital fingerprint of the object.

In other examples, user input may be used to select authenticationregions, and then the process proceeds to 234 as before. In someembodiments, an entire object may be scanned and all of the dataprocessed to find and record digital fingerprint data. Whatever thecase, the process proceeds to create a digital fingerprint, block 236,which can then be used to query the database 230 for a match. The matchresult may not be binary (yes/no); rather, in many cases, the result mayindicate a confidence level of a match or may be a composite of binaryresults or confidence levels—such as when an object has been altered inpart or in whole and/or has been assembled, or disassembled.

Example Authentication and Inspection Processes

In an embodiment, an object is scanned and an image is generated. Thesteps that follow depend on the operation to be performed. Severalillustrative example cases are discussed below.

Case 1: For authentication of a previously fingerprinted object, thefollowing steps may be followed (see FIG. 3 , discussed below):

-   1. One or more authentication regions are determined, such as    automatically by a system, or by utilizing the authentication region    definitions stored in a fingerprint template.-   2. Relevant features are extracted from each authentication region    and a digital fingerprint is generated. Feature extractions    preferably will be in the form of feature vectors, but other data    structures may be used, as appropriate.-   3. Optionally, other information, for example a unique identifier    such as a serial number may be extracted and stored to augment    subsequent search and identification functions.-   4. The digital fingerprint of the object to be authenticated is    compared to digital fingerprints stored in a database.-   5. The system reports whether (or to what extent) the object matches    one or more of the digital fingerprints stored in the database.-   6. The system may store the digital fingerprint of the object to be    authenticated in the database along with the results of the    authentication process. Preferably, only the extracted features will    be stored in the database, but the authentication image and/or the    original image and/or other data and metadata may be stored in the    database, for example for archival or audit purposes.

FIG. 3 illustrates such a process 300 in diagrammatic form. Beginning atstart block 302, the process scans an object and creates anauthentication image, block 304. The image is represented at 350, usinga passport as an example. Features are extracted, block 306, andoptionally, other information, such as a serial number or similar IDnumber, preferably unique, may be extracted as well, block 310.

The extracted data is processed to generate a digital fingerprint, block312. A database 320 may be queried for a matching fingerprint, block314. A “match” may be defined by a binary, probability, or similaritymetric or be a composite of metrics. Results of the database query maybe reported to a user, block 322. Finally, a new digital fingerprint maybe added to the database 320, shown at process block 330.

Case 2: For inspection of specific features of a previouslyfingerprinted object to determine whether they have been altered, thesteps are similar to Case 1, but the process is aimed at detection ofalterations rather than authentication of the object:

-   1. One or more authentication regions are determined, such as    automatically by the system, or by utilizing the authentication    region definitions stored in a fingerprint template.-   2. The features to be inspected are extracted from an authentication    region and the digital fingerprint is generated. The features    extracted may be in the form of feature vectors for the features to    be inspected but other data structures may be used, as appropriate.-   3. Optionally, other information, for example a unique identifier    such as a serial number may be extracted and stored to be used to    augment subsequent search and identification functions.-   4. The digital fingerprint of features to be inspected for    alteration is compared to the fingerprint of the corresponding    features from the original object stored in the database.-   5. The system reports whether the object has been altered; i.e. the    extent to which the digital fingerprint of the features to be    inspected match those previously stored in the database from the    original object, in whole or in part.-   6. The system may store the digital fingerprint of the features to    be inspected in the database along with the results of the    inspection process. Preferably, only the features will be stored in    the database, but the authentication image and/or the original image    and/or other data and metadata may be stored in the database for    archival or audit purposes.-   7. Cases 3 and 4 are elaborated in related patent applications.

In all of the above cases, features may be extracted from images ofobjects scanned under variable conditions, such as different lightingconditions. Therefore, it is unlikely two different scans will producecompletely identical digital fingerprints. In a preferred embodiment,the system is arranged to look up and match objects in the database whenthere is a “near miss.” For example, two feature vectors [0, 1, 5, 5, 6,8] and [0, 1, 6, 5, 6, 8] are not identical but by applying anappropriate difference metric the system can determine that they areclose enough to say with a degree of certainty that they are from thesame object that has been seen before. One example would be to calculateEuclidean distance between the two vectors in multi-dimensional space,and compare the result to a threshold value. This is similar to theanalysis of human fingerprints. Each fingerprint taken is slightlydifferent, but the identification of key features allows a statisticalmatch with a high degree of certainty.

FIG. 4A illustrates an image of the numeral “3” representing a numberprinted on an “original” or known U.S. dollar bill. The bill may havebeen fingerprinted, for example, at the time of manufacture or publicrelease, as described herein, or otherwise sufficiently authenticatedfor use as a reference. As noted below, fingerprint databases ofcurrency and the like may be secured. Such databases preferably excluderaw image data. This image, on the order of about 40-fold magnification,shows a number of distinctive features visible to the naked eye.

FIG. 4B illustrates an image of a number printed on a second or unknownU.S. dollar bill. The second bill may be fingerprinted using the sameprocess, and then the resulting digital fingerprints, i.e., therespective fingerprint feature vectors, may be compared as furtherexplained below, to determine whether or not the second bill is in factthe same one as the first bill. The comparison may take place eventhough the bill may have changed from wear and tear.

FIG. 5A is a simplified illustration of the results of featureextraction applied to the numeral 3 of FIG. 4A. In this figure, only theends of the numeral are shown. Two locations of interest are called outby circles 1710 and 1750. The locations of interest need not necessarilybe circular, but circular areas are advantageous for many applications.Below is a discussion on how these areas may be selected in an image.Fingerprint feature extraction is applied to each of the circularlocations of interest. The results for each location may be stored asfingerprint feature vectors. To clarify, a “location of interest”(sometimes referred to as a “point” or “area” of interest), for example1720, may well be a physical feature on the object, but the “featurevector” that characterizes that location of interest is not just avariation on the image around that location; rather, the feature vectoris derived from it by any of a number of possible means. Preferably, afeature vector may be an array of numeric values. As such, featurevectors lend themselves to comparison and other analyses in a databasesystem. A collection of feature vectors, say for location 1750, may bestored as a feature vector array.

FIG. 5B is a simplified illustration of the results of featureextraction applied to locations of interest on the numeral 3 of FIG. 4B.The same fingerprinting process may be applied to this image. The samelocations of interest as in FIG. 5A are labeled 1720 and 1760,respectively. The stored features (from the original object) arecompared with the features extracted from the new object. As in thiscase, if the locations of interest are not encountered in the secondobject, or of the feature vectors characterizing those locations ofinterest are too different, there is no match (or a low confidence levelfor a match) for that location of interest. Variables, such as whichlocations must match and/or how many locations must match and/or thedegree of matching required to conclude that an object matches the onepreviously fingerprinted, may in some embodiments be specified in adigital fingerprint record, further described below, or in some otherassociated record, to guide the decision process. This arrangement maybe advantageous, for example, for exporting a database to a genericprocessor or system for remote authentication work. The matching logicmay be embedded in the digital fingerprint record. Preferably, thematching logic is implemented in software as part of an authenticationsystem.

One advantage of the feature-based method is that when an object is wornfrom handling or use (even very worn), a system may still identify theobject as original, which may be impossible with the bitmapped approach.FIG. 6A shows a numeral from the same dollar bill image as in FIG. 4A,juxtaposed with FIG. 6B for comparison. FIG. 6B shows the numeral on thesame bill after the bill has been subjected to washing in a washingmachine, perhaps as a result of being left in the pocket of a piece ofclothing. In FIG. 15B, the image (or, rather, the dollar bill) has beendegraded; there is significant loss of ink and destruction of the papersurface in multiple locations. A bitmapped approach to matching wouldlikely fail to match these two figures due to the large number of pixelsthat are now different, as relatively few of the pixels remain the sameas the original.

FIG. 7A shows the detail of two fingerprint feature locations as before,1610 and 1650. FIG. 7B shows detail of the damaged bill with thecorresponding locations called out as 1620 and 1660, respectively. Acomparison between the similarities of area 1610 to area 1620 and ofarea 1650 to area 1660 illustrates how a comparison of the correspondingfingerprint feature vectors would be adequate to result in a match. Inpractice, a much larger number of features would be used.

The image of the damaged bill is analyzed by a processor. The processoraccesses a database of previously stored fingerprint data. If the dollarbill serial number is legible (by eye or machine), the record for thecorresponding bill may be accessed from the datastore using the serialnumber as an index. Similarly, if any portion of the serial number islegible, the search for a matching record can be narrowed on that basis.Either way, a candidate record, containing a set of stored regions ofinterest may be compared to the image of the damaged bill.

As explained above, in addition to being able to recognize a wornobject, the feature-based approach is able to address other externalproblems such as rotated images. This is especially relevant in a systemwhere an unsophisticated user, such as a retail customer, may bescanning an object to be authenticated. In such cases, external factorslike lighting and rotation may not be under the system operator'scontrol.

Referring now to FIG. 8 , which shows the original image on the leftside, with a small set of fingerprint features marked as small diamondshapes. This is merely a callout symbol for illustration. In someembodiments, as noted, preferably circular areas are used. For eachfeature (preferably identified in the database record), a search isconducted of the suspect image on the right side of FIG. 8 (or a portionof it) for a matching feature. The position may not match exactly, dueto “stretch”, an effective difference in magnification, and/or due torotation of the image, or due to other circumstances. Although it maynot match locations literally; a mathematical transformation may bedefined that maps one image to the other, thereby accounting forrotation and stretch as appropriate. Thus, a bounding rectangle Aindicated by the box in the left side image may be mapped to aquadrilateral, indicated by the line B in the right-side image.

Once an appropriate transformation is found, further matching may bedone to increase the level of confidence of the match, if desired. Insome embodiments, a number of matches on the order of tens or hundredsof match points may be considered sufficient. The number of non-matchpoints also should be taken into account. That number should preferablybe relatively low, but it may be non-zero due to random dirt, system“noise”, and other circumstances. Preferably, the allowed mapping ortransformation should be restricted depending on the type of objectunder inspection. For instance, some objects may be inflexible, whichmay restrict the possible deformations of the object.

Summarizing the imaging requirements for a typical fingerprintingsystem, for example for inspecting documents, the system preferablyshould provide sufficient imaging capability to show invariant features.Particulars will depend on the regions used for authentication. For manyapplications, 10-fold magnification may be adequate. For ink bleeds onpassports, bills, and other high-value authentication, 40-foldmagnification may likely be sufficient. In preferred embodiments, thesoftware should implement a flexible response to accommodatemisalignment (rotation), misorientation, and scale changes. Colorimaging and analysis is generally not required for using the processesdescribed above, but may be used in some cases.

Induction and Authentication

FIG. 9 is a simplified diagram illustrating the concepts of inductionand authentication. The term “induction” is used in a general manner torefer to entering an object or a set of objects into an electronicsystem for subsequently identifying, tracking, or authenticating theobject, or for other operations. The object itself is not entered intothe system in a physical sense; rather, induction refers to creating andentering information into a memory or datastore from which it can laterbe searched, interrogated, retrieved, or utilized in other kinds ofdatabase operations.

In FIG. 9 , induction 1802 thus may refer to a process that includescapturing an image of an object (or part of an object), processing theimage to extract descriptive data, storing the extracted data, or any orall of these operations. The inducted object, represented by a cube1804, then leaves the induction site, and proceeds in time and spacealong a path 1806. Induction may be done at the point of creation ormanufacture of the object, or at any subsequent point in time. In somecases, induction may be done clandestinely, such as without theknowledge of the person or entity currently having ownership and/orpossession of an object. The term “possession” is used in the broadestsense to include, for example, actual physical possession, as well ascontrol—for example, having they key to a secure physical storage wherean object is kept.

After induction, the object 1804 may encounter wear and tear, andotherwise may change, intentionally or not, in ways that may not beknown a priori, represented by the question mark 1808. The originalobject 1804 may even in fact be lost or stolen after induction and acounterfeit may be introduced. Along path 1809, an object 1810 may bepresented for authentication, represented by block 1820. Below aredescribed some additional scenarios and use cases for the authenticationtechnology described herein, and what may be done under the broadheading of “authentication”. Under many circumstances, induction,authentication, or both may be done remotely by use of technology suchas drones or by other covert means. In one example, an agent may take aphotograph of an object with a smartphone, without the knowledge orconsent of the possessor of the object, and the resulting image may beutilized for induction and/or authentication as described herein.

More specifically, in some embodiments, some part of theinduction/authentication process may be done remote from a facilityintended for that purpose. In addition, some part of theinduction/authentication process may be accomplished without theknowledge of the then-current possessor of an object. In particular, theinduction and/or authentication are not part of the current possessors'normal processes. These two criteria are not essential for the presentdisclosure, but are generally representative of some applications.

FIG. 10 is a simplified flow diagram of one example of a process forcreating a digital fingerprint that includes feature vectors based on ascanned image of an object. The process begins with initialization atblock 2120. This step may comprise initializing a datastore, calibratingan image capture system, or other preliminary operations. An object orobject is scanned, block 2122, forming digital image data. Preferably,depending on the context, the scanning may be automated. In other cases,an operator may be involved in manual scanning. From the image data, anauthentication image is generated, block 2124, which may comprise all ora selected subset of the scan data. Next, a digital fingerprint recordmay be initialized, for example in a memory or datastore, block 2126.

To begin forming a digital fingerprint of a scanned object, at least oneauthentication region is selected, block 2130, in the authenticationimage data. This selection preferably is carried out by thefingerprinting software. The authentication region(s) may be selectedaccording to a predetermined template based on the class of objects.Locations of the authentication regions may be stored in the digitalfingerprint record, block 2132.

At block 2134, the process continues by selecting locations of interestwithin each authentication region. To select locations of interest(areas in an image from which to extract fingerprint features), asoftware process may automatically select a large number—typicallyhundreds or even thousands per square mm—of preferred locations ofinterest for purposes of the digital fingerprint. A location may be ofinterest because of a relatively high level of content. That “content”in a preferred embodiment may comprise a gradient or vector, including achange in value and a direction. The selected locations of interest maybe added to the fingerprint record, block 2136. In one example, suchareas may be identified by a location or centroid, and a radius thusdefining a circular region. Circular regions are preferred for someapplications because they are not affected by rotation of the image.

Next, block 2138, the process calls for extracting features from eachlocation of interest, and forming feature vectors to describe thosefeatures in a compact form that facilitates later analysis, for example,calculation of vector distances as a metric of similarity in comparingfingerprints for authentication. Various techniques are known forextracting such features. The resulting feature vectors are added to thefingerprint, block 2140. At block 2142, additional information may beadded to the digital fingerprint identifying other fingerprints andrelated information associated with the same object. In someembodiments, a relationship, such as relative location of the otherfingerprints to the current fingerprint may be used. For example, insome objects, multiple regions may be authentic individually, but achange in their relative location may indicate that the object is notauthentic. Thus, a fingerprint record may include first and secondfeature vectors (each describing a corresponding feature extracted froman area of interest) and a relative location of one to the other.

Note that FIG. 10 gives a limited example of the information componentscontained in a digital fingerprint. In one preferred embodiment, otherinformation (not shown in FIG. 10 ) relating to the identification andtracking of an object may be incorporated, stored, or otherwiseassociated with an object's digital fingerprint record. When referencingan object's digital fingerprint, the components of the digitalfingerprint referenced may vary based on system operation or userpreference. In some cases, other information such as dimensions, color,shape and such may be stored in a digital fingerprint but notnecessarily stored as a feature vector.

Above, with regard to FIG. 8 , the transformation from one set offeature vectors to another was described, to accommodate stretch,rotation or variations in magnification. In similar fashion, relativelocations of features in a fingerprint can be stored in the record andused for comparison to a new fingerprint under consideration. Thefeature extraction may be repeated, block 2150, using an adjusted areasize or scale (such as magnification). Feature vectors created at theadjusted size may be added to the fingerprint, block 2152. Additionalfeatures may be extracted at additional magnification values, until anadequate number are provided, decision 2154. This additional data may beadded to the fingerprint, block 2156. This data may be helpful infinding a matching fingerprint where the authentication imagemagnification is not the same as the image at the time of induction ofthe object. Finally, and optionally, the scanned image itself (generatedat 2122) may be added to the database, block 2158. This process to builda digital fingerprint ends at 2160.

Authentication-Based Tracking

FIG. 11 is a simplified system diagram for positive control of objectsconsistent with the present disclosure. For illustration, we describethe objects as luggage items or “bags” as may be moving in the “bagroom” at an airport, from intake, say at a ticket counter, to the rampfor loading on an aircraft. The system of FIG. 11 includes severalTracking Units (“TU”). A TU in some embodiments may comprise one or morescanners, such as cameras, computer processors, and communication systemconnections. For example, in FIG. 11 , one TU 1106 shows plural cameras1110, a local server or processor 1130, and the processor coupled to anetworking interface 1132 for external communications. The cameras 1110may be coupled, for example, by a LAN, to the processor or local server1130. The LAN or other suitable infrastructure enables the processor tocollect image data from the cameras 1110.

In this illustration, a conveyor system 1102 is shown in simplifiedform. An induction system 1104 may be used to induct objects 1100 into afingerprint-based system as detailed above. In the air travel example,the luggage bags may be inducted at an induction system adjacent to theticket counter or other check-in location such as a kiosk or curb-sidecounter. The bags may be inducted into a reference database system 1170.The induction system(s) may be coupled via network communicationsinfrastructure 1166 which may also provide communications among multipletracking units 1106, 1140, 1142, 1160. In an example, the referencedatabase system 1170 may comprise a database 1172 to store digitalfingerprints and related data. The database system 1170 may furtherinclude software components for communications 1174, security 1176,incident recovery 1178, induction 1180, authentication 1182, andinventory management 1184. These components are typical but notexhaustive. The functions listed are not necessarily assigned tospecific components as shown.

The cameras 1110 of tracking unit 1106 are located along a portion ofthe conveyor system 1102 with contiguous or overlapping ability to seethe conveyor system except, possibly, areas that are not visible to thescanners and where the object may be under some other form of positivecontrol. Examples of an area of positive control include being withinthe hold of an aircraft or a locked and sealed shipping container. Theoverlapping or contiguous fields of view are each indicated by a pairdashed lines, for example, lines 1112 with regard to camera 1110. The TU1106 is responsible for a corresponding region of the conveyor withinview of its cameras. The next adjacent region, covered by a nexttracking unit 1140, similarly has one or more cameras (not shown) thatcan view the region indicated by dashed lines 1144. Note that TU 1160camera 1111 has a field of view that overlaps with the next trackingunit 1140 field of view. Similarly, the TU 1140 field of view 1144overlaps with the next TU 1142 field of view 1146, etc. In this way,each object (bag) traveling on the conveyor system 1102 is always inview of at least one camera of a tracking unit (TU), except, as noted,when the item is in some other area of positive control. Another exampleof such an area of positive control is represented by a tunnel 1162, inbetween the fields of view of TU 1160 and TU 1142. The tunnel preventsany object from entering or leaving the conveyor except at the ends ofthe tunnel. There, the corresponding TUs observe what goes into and whatemerges from the tunnel, so that continuous positive control ismaintained. Finally, an exit system 1164 may be provided. The exitsystem may confirm identification of objects as they leave theconveyance system, for example, during loading onto a truck or into anairplane cargo container. The exit system in an embodiment may beimplemented with a modified tracking unit. Preferably, it may benetworked to 1166 as shown.

In some embodiments, each TU receives from upstream TUs the timing andidentification information (e.g. digital fingerprints and otheridentifiers or serial numbers) of expected bags. As each bag arrives,the TU images it, extracts its identification features, identifies theobject as the one seen by the next operating camera upstream (andpossibly as the one inducted at the start of transport), determines thatits arrival time is within tolerance of the expected arrival time sentby upstream TU(s), and passes on to the next TUs downstreamidentification information and expected arrival times.

Identification Methods. In some embodiments, when an object is underinitial positive control (when the object is first manufactured or wherethe passenger checks in his bag) high resolution images of the objectare acquired and digital fingerprints extracted from them. The digitalfingerprints are associated with an object record (e.g. the passengerrecord for airports). The digital fingerprint generated at check-in isused to inform the next TU(s) down the line of the identifyinginformation for the current bag. This is a much simpler discriminationtask than identifying one back out of tens of thousands and cangenerally be performed with a sub stantially-reduced feature vector set.In addition, the downstream TU(s) are told when to expect the object.They are either told the tolerances for arrival or get that from thedatabase system.

In some embodiments, in cases where a physical object remains in acontrolled area from one TU to the next, the downstream TU may assumethe identity of the object is what the upstream TU provided, as long asthe object arrives in the field of view of the downstream TU within anarrival time tolerance limit. In cases where the downstream TU receivesan object that has passed through an uncontrolled area on its way fromthe upstream TU, or if the object arrives outside the tolerance limitsof arrival time, or if there is actual or potential confusion betweenobjects (such as when multiple objects arrive at the downstream TUwithin an arrival time range allocated to one or more objects, a TUdownstream from the uncontrolled area preferably will independentlyidentify the object or objects by referencing the digital fingerprintsof the objects. In general, referencing digital fingerprints may referto the matching (or attempted matching) of digital fingerprints in wholeor in part or components thereof, such as of feature vectors and/oradditional object-specific identifying features stored in the digitalfingerprint record. Uncontrolled areas may be, for example, areas thatare temporarily or permanently outside of the field of view of trackingunits or areas where the field of view is obscured.

In one embodiment, each TU has several functions under normal operation,including but not limited to the following:

(1) Track the position of an object for later use (e.g. passing todownstream TUs or for transportation system operation, such as operatinga diverter gate. In the taught system, the object is always in view of aTU unless some other form of positive control exists. The bag may, forexample, go through a tunnel where watching it is impossible, but ifboth the entrance and the exit to the tunnel are under positive controland the tunnel is otherwise secure from unauthorized entry, the bag isconsidered under positive control while in the tunnel. Similarly, a bagin a loading container being transported to the airplane hold isconsidered to be under positive control provided it was under positivecontrol when it entered the loading container and the loading containeris sealed in a way that prevents unauthorized entry.

(2) Identify the object as the expected one. Identification may consistof referencing digital fingerprint information recorded in the object'sdatabase record or passed on from the TU(s) upstream.

(3) Pass on the timing information and digital fingerprint informationto the next TU(s) downstream. If there are points of diversion betweenthis TU and the next ones downstream, all possible next TUs receive thisinformation. This information may be confirmed by the receiving TU(s) asa safeguard against TU failure or maintenance.

(4) Reacquire the object after some other period of positive controlsuch as the abovementioned tunnel, the explosives detector, or TSAmanual screening.

In a luggage handling scenario, for example, under most conditions, abag will not change orientation or which side is up in going from one TUto the next. In that case, whatever digital fingerprints were used asreferences by the next upstream TU may be sufficient for the current TUto use as well. In the event of exceptions, however (when the simpleidentification fails), such as a bag tumbling and now presenting adifferent side to the TU, the next TU to see the bag needs access to thedigital fingerprint of the bag that was generated at check-in forcomparison. This is true in the event of loss of positive control of anykind. If there is a jam, for example, sorting out the bags after the jamwill require the next TUs downstream to have access to the digitalfingerprints of all the bags that might have been involved in the jam.Much of the time these can be digital fingerprint information extractedand confirmed by the TU(s) upstream from the jam, but sometimes willhave to be the digital fingerprint generated at the beginning (e.g. atbag check-in). That data may be acquired, for example, by a request to acentral server or database system.

When the bag goes through a gap in coverage, the situation is much likethat in a jam. Such loss of positive control occurs at the explosivesdetectors, at TSA inspection stations, and some other places such as atthe belt that loads the airplane hold. To recover from the lack ofcoverage the relevant TUs must have access to the digital fingerprintsof all bags that could be present. The digital fingerprints includefeature vectors extracted from the corresponding bag.

Progression of Identification Techniques. In some embodiments, a TU maybe capable of an escalating hierarchy of identification techniques asmay be necessary:

In an embodiment, under normal use the TU extracts simplified digitalfingerprints from an image of the object and compares them to digitalfingerprints from expected bags sent on (or tags to a centralizeddatabase of current system objects sent on) from upstream TUs. If theidentity of the bag is confirmed and that it arrived on time, the TU (orthe system) passes the expected arrival times and digital fingerprintsto downstream TUs.

In some embodiments, if a TU identifies a bag but finds it out of placeby more than some tolerance, it sends an exception to the trackingsystem. An arrival time outside the tolerance limits may indicate a bagwas tampered with and the system may require further inspection of thebag. It may also indicate the bag slid while going down a slope.

If simple identification does not work, digital fingerprint informationfrom all sides of the expected bag may be compared. If they match, thebag (and its information) continues downstream as before.

If the bag cannot be identified but has been positively tracked sincethe last identifying station, the TU may flag the database with itsfailure but the bag continues. If a second TU also fails to identify thebag, it may be routed for exception handling. In some cases, asdescribed above, in the case of continuously and positively controlledobjects, their identity may in some cases be assumed by TUs as long asthey remain controlled and arrive within a time frame that is within thesystem's toleration limits. Whether or not a positive identification atevery TU is required, or the degree to which positive identificationsare required, may be set by the system owner.

In some embodiments, if positive control has been lost (as a matter ofcourse, i.e. not as the result of a jam), the TU compares the digitalfingerprints of possible bags with the ones it extracts. If itsuccessfully identifies the bag, the bag continues down the conveyorsystem. This includes places where the conveyor is temporarily obscured(in which case “possible bags” may be only the most recently seen by theupstream camera), as well as known break points (such as the explosivesdetector and TSA inspection stations in the case of airport baggagetracking). “Break point” refers to a situation where positive control ofan object is lost. It may be re-established later, as when TSA returns abag after inspection, or a jam is cleared.

In some embodiments, if positive control is lost because of a TUfailure, downstream TUs take over for the failed unit as though a normalloss of positive control has taken place.

In some embodiments, in the event of a jam, once the jam has beencleared, all bags identified by downstream TUs are reentered into thesystem (e.g. for where they need to be routed) and all not identifiedare tagged for exceptions processing.

In some embodiments, in each of the above cases, before reportingfailure to identify, the TU may try first the digital fingerprints fromthe immediately expected bag, then from all reasonably expected bags,and finally from other bags in the system. In cases where suchcomparison takes too long, a TU (or other system components) may telldownstream TUs to throw an exception for a bag that failsidentification.

Tracking database and system. In some embodiments, this will be thetracking system modified to hold digital fingerprint and trackinginformation received from the TUs, as well as to handle any exceptionsreported. The system adds objects on initial induction (e.g. check-in atan airport) and may remove them when they leave the system (e.g. whenloaded on a plane or when picked up at a baggage carousel). In additionto providing information for authentication-based tracking, the systemmust be able to handle manual requests for full bag identification usingsystem-wide data (e.g. for a bag that cannot otherwise be identified andmay be a lost bag from someplace else).

If object aggregation occurs (e.g. bags in a loading container or thehold of an airplane, individual drug doses in a pallet), the system mustbe capable of multi-level authentication, provided the aggregated unitcan be securely sealed during transport to prevent tampering.

FIG. 12 is a simplified flow diagram further illustrating selectedaspects of operation of a tracking unit (TU) to realize positive controlof objects. In an embodiment, the subject TU receives data from anupstream TU, block 2322. In some cases, for example, where the upstreamTU is disabled, off-line, etc., the TU may instead receive data and/orinstructions from a central or supervisory process. The current TUdetects an incoming object, block 2324, for example, by means of aswitch, camera, photocell, or other detector. In one example, the entrymay be detected by a camera arranged for that purpose such as camera1110 (FIG. 11 ). The TU acquires one or more digital fingerprints fromthe incoming object, block 2326, for example, using a camera or scanneras described above.

Next, the TU compares the acquired digital fingerprint of the incomingobject to the data received from the upstream TU, block 2328, anddetermines whether the digital fingerprint obtained from the incomingobject matches a digital fingerprint of an expected incoming object,decision 2330. If the fingerprints match, the TU may also check whetherthe object arrived within an expected timeframe, decision 2338. A delay,or an arrival before an expected time, may be a suspicious circumstancethat warrants further investigation. The timing may be a datum for inputto an AI system that assesses the risk of a foreign object intrusioninto the system. Accordingly, arrival of an object outside of anexpected timeframe spawns an exception, block 2340. The exception may berealized in a message to a reference database system or to a supervisoryprocess.

If the object arrived in a timely manner, the TU may update the relevantdatabase, block 2350, for example, to record the arrival, and perhaps atimestamp. The TU may pass on its results to a next downstream TU, block2352.

Referring again to decision 2330, if the object's digital fingerprintdid not match as expected, a more thorough check may be made, forexample, by comparing digital fingerprints from additional sides of theobject, block 2360. In some embodiments, fingerprints may be acquired atintake from multiple sides of an object. For example, an intake systemmay have multiple scanners arranged around an object to acquirefingerprints. If an object rotated during conveyance or handling, afingerprint previously acquired from a different side may then match.The additional fingerprints may be checked by querying a referencedatabase. If a match is found, decision 2362, the TU may again check thetimeliness of the arrival as before, decision 2338.

If a match is not found at 2362, the TU may conduct a broader search bycomparing the new arrival digital fingerprint with all of the objectsreasonably expected to arrive at the TU at 2370. For example, the order(sequence) of an adjacent pair of objects may have been switched bymanual handling, for instance, an object may have fallen off a conveyor,and a worker replaced it but not in exactly the same spot. The bag wouldthen match one of a set of reasonably expected objects—those “in theneighborhood” at the time. If that match is found, decision 2372, the TUmay again update the database, block 2350, and proceed as before toblock 2352.

In a case that no match is found at 2360 or at 2370, the TU may conductan even broader inquiry by comparing the current digital fingerprint toall of the objects then in the system, block 2380. The “system” ofinterest here may be a local system, for example, an airport bag room asdescribed above. More broadly, the system may include all airportsserved by the carrier. In this way, the TU may identify an object thaterroneously arrived far from its intended destination. Thus, the errantobject is located very quickly; indeed, it may be located before it wasdetermined to be “lost.” Even more broadly, the TU may query otherairline systems that have implemented digital fingerprinting to resolvea “lost or missing” bag for them. Finally, if no match is found,decision 2382, the TU may send an insertion alert, block 2390, to alertauthorities or a supervisory process, that an unrecognized or “foreign”object has been inserted into the system. The foreign object may bescanned for identification purposes.

Hardware and Software

Most of the equipment discussed in this document comprises hardware andassociated software. For example, the typical portable device is likelyto include a camera, one or more processors, and software executable onthose processors to carry out the operations described. We use the termsoftware herein in its commonly understood sense to refer to programs orroutines (subroutines, objects, plug-ins, etc.), as well as data, usableby a machine or processor. As is well known, computer programs generallycomprise instructions that are stored in machine-readable orcomputer-readable storage media. Some embodiments of the presentinvention may include executable programs or instructions that arestored in machine-readable or computer-readable storage media, such as adigital memory. We do not imply that a “computer” in the conventionalsense is required in any particular embodiment. For example, variousprocessors, embedded or otherwise, may be used in equipment such as thecomponents described herein.

Memory for storing software again is well known. In some embodiments,memory associated with a given processor may be stored in the samephysical device as the processor (“on-board” memory); for example, RAMor FLASH memory disposed within an integrated circuit microprocessor orthe like. In other examples, the memory comprises an independent device,such as an external disk drive, storage array, or portable FLASH keyfob. In such cases, the memory becomes “associated” with the digitalprocessor when the two are operatively coupled together, or incommunication with each other, for example by an I/O port, networkconnection, etc. such that the processor can read a file stored on thememory. Associated memory may be “read only” by design (ROM) or byvirtue of permission settings, or not. Other examples include but arenot limited to WORM, EPROM, EEPROM, FLASH, etc. Those technologies oftenare implemented in solid state semiconductor devices. Other memories maycomprise moving parts, such as a conventional rotating disk drive. Allsuch memories are “machine readable” or “computer-readable” and may beused to store executable instructions for implementing the functionsdescribed herein. The stored data may be volatile but it isnon-transitory; that is, it is not in the form of signals.

A “software product” refers to a memory device in which a series ofexecutable instructions are stored in a machine-readable form so that asuitable machine or processor, with appropriate access to the softwareproduct, can execute the instructions to carry out a process implementedby the instructions. Software products are sometimes used to distributesoftware. Any type of machine-readable memory, including withoutlimitation those summarized above, may be used to make a softwareproduct. That said, it is also known that software can be distributedvia electronic transmission (“download”), in which case there typicallywill be a corresponding software product at the transmitting end of thetransmission, or the receiving end, or both.

Consequently, in view of the wide variety of permutations to theembodiments described herein, this detailed description and accompanyingmaterial is intended to be illustrative only, and should not be taken aslimiting the scope of the invention.

Having described and illustrated the principles of the invention in apreferred embodiment thereof, it should be apparent that the inventionmay be modified in arrangement and detail without departing from suchprinciples. We claim all modifications and variations coming within thespirit and scope of the following claims.

We claim:
 1. A method of tracking a physical object by a tracking systemthat includes a plurality of tracking units arranged along a path andarranged to form a field of view that covers at least a portion of thepath, the method comprising: providing authentication of the physicalobject by the plurality the tracking units arranged to form the systemfield of view to continuously track the physical object as the physicalobject travels along the portion of the path; receiving a physicalobject at a first tracking unit of a tracking system; capturing, by acapture device of the first tracking unit, a first piece of digitalimage data of a portion of the physical object; creating, by the firsttracking unit, a first digital fingerprint that uniquely identifies thephysical object based on a plurality of features extracted from thefirst piece of digital image data; storing the digital fingerprint in adatastore; determining, by the tracking system, a loss of positivecontrol of the physical object; receiving, by the tracking system, thephysical object at a second tracking unit of the tracking system afterthe loss of positive control of the physical object; capturing, by acapture device of the second tracking unit, a second piece of digitalimage data of a portion of the physical object; and re-authenticatingthe physical object after the loss of positive control using the secondpiece of digital image data and the first digital fingerprint.
 2. Themethod of claim 1, further comprising: creating, by the second trackingunit, a second digital fingerprint that uniquely identifies the physicalobject based on a plurality of features extracted from the second pieceof digital image data; storing the second digital fingerprint in thedatastore; comparing, by the tracking system, the second digitalfingerprint to at least one digital fingerprint record stored in thedatastore; identifying, by the tracking system, the physical objectbased on the comparing the second digital fingerprint to the at leastone digital fingerprint record stored in the datastore; routing thephysical object to a third tracking unit of the tracking system, whereinthe third tracking unit is located downstream from the second trackingunit; and communicating a timing of the physical object on a route and apiece of identification information of the physical object to the thirdtracking unit.
 3. The method of claim 1, further comprising: creating,by the second tracking unit, a second digital fingerprint that uniquelyidentifies the physical object based on a plurality of featuresextracted from the second piece of digital image data; storing, by thesecond tracking unit, the second digital fingerprint in the datastore;comparing, by the tracking system, the second digital fingerprint to atleast one digital fingerprint record stored in the datastore; failing toidentify, by the tracking system, the physical object based on thecomparing the second digital fingerprint to the at least one digitalfingerprint record stored in the datastore; capturing, by the capturedevice of the second tracking unit, multiple pieces of digital imagedata including a piece of digital image data from at least one portionof a side of the physical object; and extracting, by the second trackingunit, multiple pieces of digital fingerprint information from themultiple pieces of digital image data.
 4. The method of claim 3, furthercomprising: failing to identify, by the tracking system, the physicalobject using the multiple pieces of digital fingerprint information andthe at least one digital fingerprint record stored in the datastore; androuting the physical object for exception handling.
 5. The method ofclaim 1, further comprising determining the loss of positive control inresponse to at least one of: receiving, by the second tracking unit, thephysical object after the physical object has passed through anuncontrolled area on its way from the first tracking unit; receiving, bythe second tracking unit, the physical object outside of a tolerancelimit of an arrival time from the first tracking unit; receiving, by thesecond tracking unit, multiple objects within an arrival time rangeallowed to the physical object; and receiving, by the second trackingunit, the physical object after a malfunction of the tracking system. 6.The method of claim 5, wherein the uncontrolled area is at least one ofan area outside a field of view of the tracking units of the trackingsystem and an area where the field of view of the tracking units of thetracking system is obscured.
 7. The method of claim 1, wherein the lossof positive control of the physical object prevents the tracking systemfrom determining at least one of a timing of the physical object along apath, a location of the physical object, and an identity of the physicalobject.
 8. The method of claim 1, wherein the loss of positive controlis one of a jam of a component of the tracking system, a failure of atracking unit, and a matter of course loss of positive control.
 9. Themethod of claim 1, further comprising conveying, by a conveyor system ofthe tracking system, the physical object along the path.
 10. The methodof claim 8, further comprising: determining an arrival of the physicalobject at the first tracking unit and determining a later arrival of thephysical object at the second tracking unit; communicating a timing ofthe physical object on the path and a piece of identificationinformation of the physical object to the second tracking unit; andsimultaneously determining, by the tracking system, an identity and alocation of the physical object at a location within a field of view ofthe second tracking unit based on the communicated timing of thephysical object on the path and the second piece of digital image data.11. The method of claim 1, further comprising: creating, by the secondtracking unit, a second digital fingerprint that uniquely identifies thephysical object based on a plurality of features extracted from thesecond piece of digital image data; and querying, by the trackingsystem, the datastore using the second digital fingerprint to identifythe physical object based on the second digital fingerprint matching thefirst digital fingerprint.
 12. The method of claim 1, furthercomprising: identifying locations of interest in the first piece ofdigital image data, wherein each location of interest in the first pieceof digital image data corresponds to a respective location on a surfaceof the physical object; extracting the plurality of features from thefirst piece of digital image data for one or more of the identifiedlocations of interest in the first piece of digital image data, whereineach identified location of interest of the identified locations ofinterest corresponds to a respective location on the surface of thephysical object and each feature being inherent to the physical object;storing each extracted feature as a feature vector; and forming thefirst digital fingerprint from the stored feature vectors.
 13. A methodof tracking a physical object by a tracking system that includes aplurality of tracking units arranged along a path and arranged to form afield of view that covers at least a portion of the path, the methodcomprising: providing authentication of the physical object by theplurality the tracking units arranged to form the system field of viewto continuously track the physical object as the physical object travelsalong the portion of the path; receiving a physical object at a firsttracking unit of a tracking system; capturing, by a capture device ofthe first tracking unit, a first piece of digital image data of aportion of a physical object; creating, by the first tracking unit, afirst digital fingerprint that uniquely identifies the physical objectbased on a plurality of features extracted from the first piece ofdigital image data; storing the digital fingerprint in a datastore;determining, by the tracking system, the physical object arrives at abreak point; receiving, by the tracking system, the physical object at asecond tracking unit of the tracking system after the physical objectclears the break point; capturing, by a capture device of the secondtracking unit, a second piece of digital image data of a portion of thephysical object; and re-authenticating the physical object after thebreak point using the second piece of digital image data and the firstdigital fingerprint.
 14. The method of claim 13, wherein the break pointis one of a TSA inspection station, an explosives detector, a jam of acomponent of the tracking system, and a failure of a tracking unit. 15.A tracking system comprising: a plurality of tracking units arrangedalong a path, the plurality of tracking units including an upstreamtracking unit and a downstream tracking unit, wherein, when travelingalong the path, a physical object arrives at the upstream tracking unitbefore the physical object arrives at the downstream tracking unit,wherein each tracking unit of the plurality of tracking units includes aprocessor and memory connected to each other and a scanner connected tothe processor and memory, wherein the plurality of tracking units arearranged to form a system field of view that covers at least a portionof the path, and wherein the plurality of tracking units are arranged toprovide authentication of the physical object by each of the trackingunits along the portion of the path as the physical object travels alongthe portion of the path to continuously track the physical object andthe portion of the path; instructions stored in the memory of, andexecuted by the processor of, the upstream tracking unit, wherein theinstructions, when executed by the processor, cause the upstreamtracking unit to: capture, by the scanner of the upstream tracking unit,a first piece of digital image data of a portion of the physical object;create a first digital fingerprint that uniquely identifies the physicalobject based on a plurality of features extracted from the first pieceof digital image data; store the digital fingerprint in a datastore; andcommunicate a timing of the physical object on the path and a piece ofidentification information of the physical object to the downstreamtracking unit; and instructions stored in the memory of, and executed bythe processor of, the downstream tracking unit, wherein theinstructions, when executed by the processor of the downstream trackingunit, cause the downstream tracking unit to: determine a loss ofpositive control of the physical object; receive the physical objectwithin a field of view of the downstream tracking unit after the loss ofpositive control is determined; capture, by a scanner of the downstreamtracking unit, a second piece of digital image data of a portion of thephysical object; and re-authenticate, using the second piece of digitalimage data and the first digital fingerprint, the physical object afterthe loss of positive control is determined.
 16. The tracking system ofclaim 15, further comprising: a conveyor system that connects theplurality of tracking units of the tracking system along a path adjacentto each tracking unit.
 17. The tracking system of claim 15, wherein theprocessor of the downstream tracking unit is further configured to:create a second digital fingerprint that uniquely identifies thephysical object based on a plurality of features extracted from thesecond piece of digital image data; compare the second digitalfingerprint and the first digital fingerprint; identify the physicalobject based on the comparison of the second digital fingerprint and thefirst digital fingerprint; route the physical object to a third trackingunit located downstream from the downstream tracking unit; andcommunicate a timing of the physical object on the path and a piece ofidentification information of the physical object to the third trackingunit.
 18. The tracking system of claim 15, wherein the processor of thedownstream tracking unit is further configured to: create a seconddigital fingerprint that uniquely identifies the physical object basedon a plurality of features extracted from the second piece of digitalimage data; store the second digital fingerprint in the datastore;compare the second digital fingerprint and the first digitalfingerprint; determine a non-identification result for the physicalobject based on the comparison of the second digital fingerprint and thefirst digital fingerprint; capture, by the scanner of the downstreamtracking unit, multiple pieces of digital image data including a pieceof digital image data from at least one portion of aside of the physicalobject; and extract multiple pieces of digital fingerprint informationfrom the multiple pieces of digital image data.